What’s going on on PyPI

Scanning all new published packages on PyPI I know that the quality is often quite bad. I try to filter out the worst ones and list here the ones which might be worth a look, being followed or inspire you in some way.

• bocd

Bayesian online changepoint detection

• ipydatetime

A Jupyter widgets library for time and datetime pickers

• jupyter-math

display and evaluate math on jupyter notebook

• qurry

A prototype quantum programming language. Qurry is a prototype of a quantum probabilistic programming language, done with the ( https://unitary.fund ). The official project duration is one year, but the language may be usable before then (and in fact, can already be used to use all of the QUIL spec with some useful abstractions on top, like if statements, variable names, and so on).

• rnnsim

Random Neural Network Simulator implemented in Python.

• autt

An feature extraction algorithm

• etepcl

Command line end to end processing

• junix

Simple library to export images from Jupyter notebook

• jupyter-tabnine

Jupyter notebook extension which support coding auto-completion based on Deep Learning. This extension for Jupyter Notebook enables the use of coding auto-completion based on Deep Learning. Other client plugins of TabNine require starting a child process for TabNine binary and using Pipe for communication. This can’t be done with Jupyter Notebook, since child process can’t be created with JQuery and Jupyter Notebook doesn’t provide any way for adding third-part js libs to plugins.

• pygncd

a hierachical community detection algorithm by Girvan Newman. A Girvan Newman step is defined as a couple of successive edge removes such that a new community occurs.

• sharks

A Python package for data analysis.

• SIK

An feature extraction algorithm

• torch-lr-finder

Pytorch implementation of the learning rate range test. A PyTorch implementation of the learning rate range test detailed in [Cyclical Learning Rates for Training Neural Networks]( https://…/1506.01186 ) by Leslie N. Smith and the tweaked version used by [fastai]( https://…/fastai ). The learning rate range test is a test that provides valuable information about the optimal learning rate. During a pre-training run, the learning rate is increased linearly or exponentially between two boundaries. The low initial learning rate allows the network to start converging and as the learning rate is increased it will eventually be too large and the network will diverge.

• wtk

A Wasserstein Subsequence Kernel for Time Series

• darknet-config-generator

Darknet Neural Network Configuration Generator. If you have used darknet for one of your projects, you also understand the pain of editing the config file when you want to modify your network, optimization, and image augmentation parameters only to realize you forgot to edit another parameter after commencing training (bummer). You will also understand the pain of editing the configuration file to run inference. I implemented this to allow me to describe my neural network in a keras-like fashion and have a darknet config file generated.

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